Tongue FACS Notebook
Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.
tissue_of_interest = "Tongue"Warning message:
package ‘Seurat’ was built under R version 3.4.3 library(here)here() starts at /Users/olgabot/code/tabula-murissource(here("00_data_ingest", "02_tissue_analysis_rmd", "boilerplate.R"))package ‘dplyr’ was built under R version 3.4.2
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, uniontiss = load_tissue_facs(tissue_of_interest)Performing log-normalization
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**************************************************|[1] "Scaling data matrix"
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|=================================================================================| 100%Calculating gene means
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Calculating gene variance to mean ratios
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Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.
PCElbowPlot(object = tiss)
Choose the number of principal components to use.
# Set number of principal components.
n.pcs = 10The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity–more connections within the group than between groups. The resolution parameter determines the scale…higher resolution will give more clusters, lower resolution will give fewer.
For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.
# Set resolution
res.used <- 0.7
tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs,
resolution = res.used, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)To visualize
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 20, perplexity=30)Warning in scan(file = file, what = what, sep = sep, quote = quote, dec = dec, :
EOF within quoted string
Quitting from lines 137-174 (Tongue_facs.Rmd) # note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T, pt.size = 1.2, label.size = 4)
Check expression of genes of interset.
Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.
How big are the clusters?
table(tiss@ident)
0 1 2 3 4 5 6 7
368 253 196 182 161 121 108 27 tiss1=BuildClusterTree(tiss)[1] "Finished averaging RNA for cluster 0"
[1] "Finished averaging RNA for cluster 1"
[1] "Finished averaging RNA for cluster 2"
[1] "Finished averaging RNA for cluster 3"
[1] "Finished averaging RNA for cluster 4"
[1] "Finished averaging RNA for cluster 5"
[1] "Finished averaging RNA for cluster 6"
[1] "Finished averaging RNA for cluster 7"
Which markers identify a specific cluster?
#cluster1.markers <- FindMarkers(object = tiss, ident.1 = 1, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster2.markers <- FindMarkers(object = tiss, ident.1 = 2, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster3.markers <- FindMarkers(object = tiss, ident.1 = 3, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster4.markers <- FindMarkers(object = tiss, ident.1 = 4, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster5.markers <- FindMarkers(object = tiss, ident.1 = 5, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster6.markers <- FindMarkers(object = tiss, ident.1 = 6, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
cluster7.markers <- FindMarkers(object = tiss, ident.1 = 6, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 01m 24s#cluster0.markers <- FindMarkers(object = tiss, ident.1 = 0, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)print(x = head(x = cluster6.markers, n = 30))You can also compute all markers for all clusters at once. This may take some time.
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 99% ~01s
|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 02m 05s
| | 0 % ~calculating
|+ | 1 % ~01m 43s
|++ | 2 % ~01m 41s
|++ | 3 % ~01m 39s
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| | 0 % ~calculating
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|++++++++++++++++++++++++++++++++++++++++++++++++++| 100% elapsed = 03m 15sDisplay the top markers you computed above.
tiss.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)Generate an expression heatmap for given cells and genes. In this case, we are plotting the top 20 markers (or all markers if less than 20) for each cluster.
Assigning cell type identity to clusters
At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:
0: basal cell of epidermis (CL:0002187) 1: basal cell of epidermis (CL:0002187) 2: proliferating basal cell of epidermis (CL:0002187) 3: differentiated keratinocytes (CL:0000312) 4: differentiating basal cell of epidermis (CL:0002187) 5: suprabasal differentiating keratinocytes (CL:0000312) 6: basal cell of epidermis (CL:0002187) 7: filiform differentiated keratinocytes (CL:0000312)
# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")
# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7)
free_annotation <- c(
"basal cells",
"basal cells",
"proliferating",
"differentiated keratinocyte",
"differentiating basal cells",
"suprabasal differentiating keratinocytes",
"basal cells",
"filiform differentiated keratinocytes"
)
cell_ontology_class <- c(
"basal cell of epidermis",
"basal cell of epidermis",
"basal cell of epidermis",
"keratinocyte",
"basal cell of epidermis",
"keratinocyte",
"basal cell of epidermis",
"keratinocyte"
)
validate_cell_ontology(cell_ontology_class)
cell_ontology_id = convert_to_cell_ontology_id(cell_ontology_class)
tiss@meta.data[,'free_annotation'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = free_annotation)
tiss@meta.data[,'cell_ontology_class'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_class)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)
tiss@meta.data[tiss@cell.names,'free_annotation'] <- as.character(tiss@meta.data$free_annotation)
tiss@meta.data[tiss@cell.names,'cell_ontology_class'] <- as.character(tiss@meta.data$cell_ontology_class)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)
TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='cell_ontology_class')
Checking for batch effects
Color by metadata, like plate barcode, to check for batch effects.
TSNEPlot(object = tiss, do.return = TRUE, group.by = "mouse.sex")
Print a table showing the count of cells in each identity category from each plate.
table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))
B002777 B002780 D041894 D041904 D041911
0 61 58 90 79 80
1 46 12 67 56 72
2 85 24 28 45 14
3 40 19 38 46 39
4 20 8 44 48 41
5 10 5 35 34 37
6 1 7 29 14 57
7 3 3 7 11 3Save the Robject for later
When you save the annotated tissue, please give it a name.
filename = here('00_data_ingest', '04_tissue_robj_generated',
paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)[1] "/Users/olgabot/code/tabula-muris/00_data_ingest/04_tissue_robj_generated/facsTongue_seurat_tiss.Robj"save(tiss, file=filename)# To reload a saved object
filename = here('00_data_ingest', '04_tissue_robj_generated',
paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
load(file=filename)Export the final metadata
So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.
head(tiss@meta.data)filename = here('00_data_ingest', '03_tissue_annotation_csv',
paste0(tissue_of_interest, "_facs_annotation.csv"))
write.csv(FetchData(tiss, c('plate.barcode','cell_ontology_class','cell_ontology_id', 'free_annotation', 'tSNE_1', 'tSNE_2')), file=filename)sessionInfo()R version 3.4.1 (2017-06-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] bindrcpp_0.2 ontologyIndex_2.4 dplyr_0.7.4 useful_1.2.3
[5] here_0.1 Seurat_2.2.1 cowplot_0.8.0 ggplot2_2.2.1
[9] Matrix_1.2-10
loaded via a namespace (and not attached):
[1] diffusionMap_1.1-0 Rtsne_0.13 VGAM_1.0-4 colorspace_1.3-2
[5] class_7.3-14 modeltools_0.2-21 ggridges_0.4.1 rprojroot_1.2
[9] mclust_5.3 htmlTable_1.9 base64enc_0.1-3 proxy_0.4-17
[13] DRR_0.0.2 flexmix_2.3-14 prodlim_1.6.1 mvtnorm_1.0-6
[17] lubridate_1.7.1 ranger_0.8.0 codetools_0.2-15 splines_3.4.1
[21] R.methodsS3_1.7.1 mnormt_1.5-5 robustbase_0.92-7 knitr_1.17
[25] RcppRoll_0.2.2 tclust_1.3-1 jsonlite_1.5 Formula_1.2-2
[29] caret_6.0-77 ica_1.0-1 ddalpha_1.3.1 cluster_2.0.6
[33] kernlab_0.9-25 R.oo_1.21.0 sfsmisc_1.1-1 compiler_3.4.1
[37] backports_1.1.1 assertthat_0.2.0 lazyeval_0.2.0 lars_1.2
[41] acepack_1.4.1 htmltools_0.3.6 tools_3.4.1 igraph_1.1.2
[45] gtable_0.2.0 glue_1.1.1 reshape2_1.4.2 Rcpp_0.12.13
[49] trimcluster_0.1-2 gdata_2.18.0 ape_4.1 nlme_3.1-131
[53] iterators_1.0.8 fpc_2.1-10 timeDate_3012.100 gower_0.1.2
[57] stringr_1.2.0 irlba_2.2.1 gtools_3.5.0 DEoptimR_1.0-8
[61] MASS_7.3-47 scales_0.5.0 ipred_0.9-6 parallel_3.4.1
[65] RColorBrewer_1.1-2 yaml_2.1.14 pbapply_1.3-3 gridExtra_2.3
[69] rpart_4.1-11 segmented_0.5-2.2 latticeExtra_0.6-28 stringi_1.1.5
[73] foreach_1.4.3 checkmate_1.8.4 caTools_1.17.1 lava_1.5.1
[77] SDMTools_1.1-221 rlang_0.1.4 pkgconfig_2.0.1 dtw_1.18-1
[81] prabclus_2.2-6 bitops_1.0-6 evaluate_0.10.1 lattice_0.20-35
[85] ROCR_1.0-7 purrr_0.2.4 bindr_0.1 labeling_0.3
[89] recipes_0.1.0 htmlwidgets_0.9 tidyselect_0.2.0 CVST_0.2-1
[93] plyr_1.8.4 magrittr_1.5 R6_2.2.2 gplots_3.0.1
[97] Hmisc_4.0-3 dimRed_0.1.0 foreign_0.8-69 withr_2.0.0
[101] sn_1.5-0 mixtools_1.1.0 survival_2.41-3 scatterplot3d_0.3-40
[105] nnet_7.3-12 tsne_0.1-3 tibble_1.3.4 KernSmooth_2.23-15
[109] rmarkdown_1.6 grid_3.4.1 data.table_1.10.4 FNN_1.1
[113] ModelMetrics_1.1.0 metap_0.8 digest_0.6.12 diptest_0.75-7
[117] tidyr_0.7.2 numDeriv_2016.8-1 R.utils_2.5.0 stats4_3.4.1
[121] munsell_0.4.3 ---
title: "Tongue FACS Notebook"
output: html_notebook
---

Enter the directory of the maca folder on your drive and the name of the tissue you want to analyze.

```{r}
tissue_of_interest = "Tongue"
library(here)
source(here("00_data_ingest", "02_tissue_analysis_rmd", "boilerplate.R"))
tiss = load_tissue_facs(tissue_of_interest)
```


```{r, echo=FALSE, fig.height=16, fig.width=8}
PCHeatmap(object = tiss, pc.use = 1:10, cells.use = 500, do.balanced = TRUE, 
    label.columns = FALSE, use.full = FALSE)
```

Later on (in FindClusters and TSNE) you will pick a number of principal components to use. This has the effect of keeping the major directions of variation in the data and, ideally, supressing noise. There is no correct answer to the number to use, but a decent rule of thumb is to go until the plot plateaus.

```{r}
PCElbowPlot(object = tiss)
```

Choose the number of principal components to use.
```{r}
# Set number of principal components. 
n.pcs = 10
```


The clustering is performed based on a nearest neighbors graph. Cells that have similar expression will be joined together. The Louvain algorithm looks for groups of cells with high modularity--more connections within the group than between groups. The resolution parameter determines the scale...higher resolution will give more clusters, lower resolution will give fewer.

For the top-level clustering, aim to under-cluster instead of over-cluster. It will be easy to subset groups and further analyze them below.

```{r}
# Set resolution 
res.used <- 0.7

tiss <- FindClusters(object = tiss, reduction.type = "pca", dims.use = 1:n.pcs, 
    resolution = res.used, print.output = 0, save.SNN = TRUE, force.recalc = TRUE)
```

To visualize 
```{r}
# If cells are too spread out, you can raise the perplexity. If you have few cells, try a lower perplexity (but never less than 10).
tiss <- RunTSNE(object = tiss, dims.use = 1:n.pcs, seed.use = 20, perplexity=30)
# note that you can set do.label=T to help label individual clusters
TSNEPlot(object = tiss, do.label = T, pt.size = 1.2, label.size = 4)

```


Check expression of genes of interset.

```{r, echo=FALSE, fig.height=16, fig.width=10}
genes_to_check = c('Itgb4', 'Itga6', 'Itgb1', 'Cdh3', 'Top2a', 'Mt2', 'Krt15', 'Krt5', 'Krt14', 'Sbsn', 'Krt10','Klf4'
, 'Krt84', 'Krt36', 'Hoxc13', 'Krt4', 'Krt13', 'Fabp5', 'Krt32',  'Krt78', 'Vim', 'Hhip', 'Ptch1', 'Gli1','Dsc1', 'Dcn', 'Runx2', 'Sox2', 'Pax9')

FeaturePlot(tiss, genes_to_check, pt.size = 1.2, nCol = 4)
```

Dotplots let you see the intensity of exppression and the fraction of cells expressing for each of your genes of interest.

```{r, echo=FALSE, fig.height=4, fig.width=20}
# To change the y-axis to show raw counts, add use.raw = T.
DotPlot(tiss, genes_to_check, plot.legend = T)
```

How big are the clusters?
```{r}
table(tiss@ident)
```

```{r}
tiss1=BuildClusterTree(tiss)
```

```{r, echo=FALSE, fig.height=20, fig.width=20}
# To change the y-axis to show raw counts, add use.raw = T.
VlnPlot(tiss, genes_to_check)
```

Which markers identify a specific cluster?

```{r}
#cluster1.markers <- FindMarkers(object = tiss, ident.1 = 1, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster2.markers <- FindMarkers(object = tiss, ident.1 = 2, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster3.markers <- FindMarkers(object = tiss, ident.1 = 3, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster4.markers <- FindMarkers(object = tiss, ident.1 = 4, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster5.markers <- FindMarkers(object = tiss, ident.1 = 5, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster6.markers <- FindMarkers(object = tiss, ident.1 = 6, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
cluster7.markers <- FindMarkers(object = tiss, ident.1 = 6, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
#cluster0.markers <- FindMarkers(object = tiss, ident.1 = 0, min.pct = 0.25, only.pos = TRUE, thresh.use = 0.25)
```

```{r}
print(x = head(x = cluster6.markers, n = 30))
```

You can also compute all markers for all clusters at once. This may take some time.
```{r}
tiss.markers <- FindAllMarkers(object = tiss, only.pos = TRUE, min.pct = 0.25, thresh.use = 0.25)
```


Display the top markers you computed above.
```{r}
tiss.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)
```

## Generate an expression heatmap for given cells and genes. In this case, we are plotting the top 20 markers (or all markers if less than 20) for each cluster.

```{r, echo=FALSE, fig.height=50, fig.width=35}
#top10 <- tiss.markers %>% group_by(cluster) %>% top_n(10, avg_logFC)
top20 <- tiss.markers %>% group_by(cluster) %>% top_n(20, avg_logFC)
# setting slim.col.label to TRUE will print just the cluster IDS instead of
# every cell name
DoHeatmap(tiss, genes.use = top20$gene, remove.key = TRUE, cex.row = 20, group.cex = 20 )
```
## Assigning cell type identity to clusters

At a coarse level, we can use canonical markers to match the unbiased clustering to known cell types:

0: basal cell of epidermis (CL:0002187)
1: basal cell of epidermis (CL:0002187)
2: proliferating basal cell of epidermis (CL:0002187)
3: differentiated keratinocytes (CL:0000312)
4: differentiating basal cell of epidermis (CL:0002187)
5: suprabasal differentiating keratinocytes  (CL:0000312)
6: basal cell of epidermis (CL:0002187)
7: filiform differentiated keratinocytes (CL:0000312)

```{r}
# stash current cluster IDs
tiss <- StashIdent(object = tiss, save.name = "cluster.ids")
# enumerate current cluster IDs and the labels for them
cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7)
free_annotation <- c(
"basal cells", 
"basal cells", 
"proliferating", 
"differentiated keratinocyte", 
"differentiating basal cells", 
"suprabasal differentiating keratinocytes", 
"basal cells",
"filiform differentiated keratinocytes" 
)

cell_ontology_class <- c(
  "basal cell of epidermis", 
  "basal cell of epidermis", 
  "basal cell of epidermis", 
  "keratinocyte",
  "basal cell of epidermis",
  "keratinocyte",
  "basal cell of epidermis",
  "keratinocyte"
)

validate_cell_ontology(cell_ontology_class)
cell_ontology_id = convert_to_cell_ontology_id(cell_ontology_class)

tiss@meta.data[,'free_annotation'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = free_annotation)
tiss@meta.data[,'cell_ontology_class'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_class)
tiss@meta.data[,'cell_ontology_id'] <- plyr::mapvalues(x = tiss@ident, from = cluster.ids, to = cell_ontology_id)
tiss@meta.data[tiss@cell.names,'free_annotation'] <- as.character(tiss@meta.data$free_annotation)
tiss@meta.data[tiss@cell.names,'cell_ontology_class'] <- as.character(tiss@meta.data$cell_ontology_class)
tiss@meta.data[tiss@cell.names,'cell_ontology_id'] <- as.character(tiss@meta.data$cell_ontology_id)
TSNEPlot(object = tiss, do.label = TRUE, pt.size = 0.5, group.by='cell_ontology_class')

```


## Checking for batch effects


Color by metadata, like plate barcode, to check for batch effects.
```{r}
TSNEPlot(object = tiss, do.return = TRUE, group.by = "mouse.sex")
```

Print a table showing the count of cells in each identity category from each plate.

```{r}
table(as.character(tiss@ident), as.character(tiss@meta.data$plate.barcode))
```



# Save the Robject for later
When you save the annotated tissue, please give it a name.

```{r}
filename = here('00_data_ingest', '04_tissue_robj_generated', 
                     paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
print(filename)
save(tiss, file=filename)
```

```{r}
# To reload a saved object
filename = here('00_data_ingest', '04_tissue_robj_generated', 
                      paste0("facs", tissue_of_interest, "_seurat_tiss.Robj"))
load(file=filename)
```



# Export the final metadata

So that Biohub can easily combine all your cell_ontology_classs, please export them as a simple csv.

```{r}
head(tiss@meta.data)
```


```{r}
filename = here('00_data_ingest', '03_tissue_annotation_csv', 
          paste0(tissue_of_interest, "_facs_annotation.csv"))
write.csv(FetchData(tiss, c('plate.barcode','cell_ontology_class','cell_ontology_id', 'free_annotation', 'tSNE_1', 'tSNE_2')), file=filename)

```

```{r}
sessionInfo()
```

